Co-registration of pre- and post-stent intravascular OCT images for validation of finite element model simulation of stent expansion

摘要

Intravascular optical coherence tomography (IVOCT) provides high-resolution images of coronary calcifications anddetailed measurements of acute stent deployment following stent implantation. Since pre- and post-stent IVOCT image“pull-back” acquisitions start from different locations, registration of corresponding pullbacks is needed for assessingtreatment outcomes. In particular, we are interested in assessing finite element model (FEM) prediction of lumen gainfollowing stenting, requiring registration. We used deep learning to segment calcifications in corresponding pre- and poststentIVOCT pullbacks. We created 1D representations of calcium thickness as a function of the angle of the helical IVOCTscans. Registration of two scans was done by maximizing the cross correlation of these two 1D representations.Registration was accurate, as determined by visual comparisons of 2D image frames. We used our pre-stent calcificationsegmentations to create a lesion-specific FEM, which took into account balloon size, balloon pressure, and stentmeasurements. We then compared simulated lumen gain from FEM analysis to actual stent deployment results. Actuallumen gain across ~200 registered pre and post-stent images was 1.52 ± 0.51, while FEM prediction was 1.43 ± 0.41.Comparison between actual and FEM results showed no significant difference (p < 0.001), suggesting accurate predictionof FEM modeling. Registered image data showed good visual agreement regarding lumen gain and stent strutmalapposition. Hence, we have developed a platform for evaluation of FEM prediction of lumen gain. This platform canbe used to guide development of FEM prediction software, which could ultimately help physicians with stent treatmentplanning of calcified lesions.